Repository logo
 
Publication

A Machine Learning App for Monitoring Physical Therapy at Home

2023-12-27Journal article Open access
Simple item page
dc.contributor.authorPereira, Bruno
dc.contributor.authorCunha, Bruno
dc.contributor.authorViana, Paula
dc.contributor.authorLopes, Maria
dc.contributor.authorMelo, Ana S. C.
dc.contributor.authorSousa, Andreia S. P.
dc.date.accessioned2024-01-29T08:23:49Z
dc.date.available2024-01-29T08:23:49Z
dc.date.issued2023-12-27
dc.description.abstractShoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citation1. Pereira B, Cunha B, Viana P, Lopes M, Melo ASC, Sousa ASP (2023). A Machine Learning App for Monitoring Physical Therapy at Home. Sensors. 2024; 24(1):158. https://doi.org/10.3390/s24010158pt_PT
dc.identifier.doi10.3390/s24010158pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/24729
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.subjectpose estimation; exercise evaluation; mobile health; remote monitoring; rehabilitationpt_PT
dc.titleA Machine Learning App for Monitoring Physical Therapy at Homept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.startPage158pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume24pt_PT
person.familyNameViana
person.givenNamePaula
person.identifier936138
person.identifier.ciencia-idEA17-B097-BD2E
person.identifier.orcid0000-0001-8447-2360
person.identifier.scopus-author-id7003678537
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication17ac1586-7589-4027-a541-3aea351fd6ae
relation.isAuthorOfPublication.latestForDiscovery17ac1586-7589-4027-a541-3aea351fd6ae

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A Machine Learning App for Monitoring Physical Therapy at Home_signed_signed_signed.pdf
Size:
858.21 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP - DEE - Artigos